Drug cell line interaction prediction

Abstract

Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these areas starts from the fingerprints or features of drugs, instead of their structures. In this paper, we introduce a model for phenotypic screening, which is called twin Convolutional Neural Network for drugs in SMILES format (tCNNS). tCNNS is comprised of CNN input channels for drugs in SMILES format and cancer cell lines respectively. Our model achieves $0.84$ for the coefficient of determinant($R^2$) and $0.92$ for Pearson correlation($R_p$), which are significantly better than previous works\cite{ammad2014integrative,haider2015copula,menden2013machine}. Besides these statistical metrics, tCNNS also provides some insights into phenotypic screening.

References in corpus (6)

• A Survey on Deep Learning in Medical Image Analysis
• Molecular Graph Convolutions: Moving Beyond Fingerprints
• Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
• AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
• A Convolutional Neural Network for Modelling Sentences
• Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity